Efficacy of Radicava® IV (intravenous edaravone) in subjects with differing trajectories of disease progression in amyotrophic lateral sclerosis: Use of a novel statistical approach for post hoc analysis of a pivotal phase 3 clinical trial
Bibliographic record
Abstract
INTRODUCTION: Subjects with amyotrophic lateral sclerosis (ALS) treated with Radicava® (edaravone) IV (intravenous; Mitsubishi Tanabe Pharma America [MTPA], hereafter "MTPA IV edaravone") in Study MCI186-19 had a significantly slower physical functional decline vs placebo-treated subjects as measured by the revised ALS Functional Rating Scale (ALSFRS-R) and analyzed by the linear mixed model for repeated measures (MMRM). This Study 19 post hoc analysis of MTPA IV edaravone-treated and placebo-treated subjects evaluated linear and nonlinear latent class mixed models defining trajectories based on identifying the model with the lowest Bayesian information criterion. The best model differentiated 4 nonlinear trajectories in ALS subjects. ALSFRS-R total score in MTPA IV edaravone-treated and placebo-treated subjects was evaluated for these 4 nonlinear latent class trajectory groups. METHODS: Disease trajectories of MCI186-19 MTPA IV edaravone-treated or placebo-treated ALS subjects who completed the double-blind period were investigated using latent class analysis (LCA) statistical models to identify potential unique nonlinear ALSFRS-R disease trajectories. RESULTS: ALSFRS-R trajectories revealed 4 unique nonlinear trajectory latent classes per treatment group in MTPA IV edaravone-treated and placebo-treated ALS subjects completing the MCI186-19 double-blind period. Latent classes 2-4 had statistically significant slowing of ALSFRS-R total score decline in the predicted nonlinear trajectories of MTPA IV edaravone-treated vs placebo-treated ALS subjects. CONCLUSIONS: This post hoc analysis suggests MTPA IV edaravone treatment results in slower ALSFRS-R decline vs placebo in most predicted nonlinear trajectories. LCA is a novel approach that may benefit future trial analyses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".